Ubiquitous Computing In Advanced Manufacturing

Abstract:
Inexpensive computing and sensing technology is making it possible to
measure and store the history of manufacturing equipment and products,
infrastructure components, and medical patients. A challenge for
computer science is to make effective use of this flood of data. We
will develop and demonstrate algorithms, technology, and paradigms for
the use of this information in advanced manufacturing. The scope of
manufacturing and design will be extended throughout the lifetime of
the product. Information from a product such as a piece of factory
automation equipment will be used to develop software upgrades for
that individual piece of equipment, as well as to refine the designs
of new equipment. We will develop machine learning algorithms to
detect patterns, optimize performance, customize product behavior, and
predict failures. We will also develop large scale simulations to
test and verify these learning algorithms, as well as to allow the
construction of virtual factories to evaluate manufacturing process
designs. We will develop networking services that allow products to
communicate with their factories or maintenance organizations, and
allocate processing and sharing of data efficiently.

Research Plan: The Vision

As the prices of computers and sensors continue to decrease, the
opportunity arises to widely distribute embedded computing and sensing
in the manufacturing process, at the point of sale, and in the
products themselves. A challenge will be to make effective use of
this flood of data and allow a manufacturer tight control of design
refinements, the manufacturing process, and maintenance of products in
the field. We expect each installation of a product to be unique, and
the product must optimize its effectiveness in that environment based
on data it collects and on the experience of similar products in
similar environments. We expect feedback from how a product performs
or how it is used to guide rapid redesign, so that a steady stream of
improved products with short design times results. We also expect to
dramatically decrease the cost of manufacturing equipment and product
maintenance and failure by automatically predicting when failures are
going to occur and preventing their occurrence. Data driven routine
maintenance with constant supervision automatically performed by
machine will be much cheaper and more reliable than the current
practice of periodically scheduled maintenance and/or human
monitoring.

An Initial Application

We intend to first focus on products that are used in manufacturing
processes, such as electric motors or automation equipment. An example
of such a piece of equipment is a packager that sorts discrete objects
into bags or packages. Each package must meet weight requirements, and
the incoming objects have a range of weights. The packager must decide
which object to route to which unfilled package to minimize the amount
of excess weight in the package. If a packager has a mechanical
failure, the throughput of the manufacturing process must be decreased
and the entire line will not operate at full capacity.

This piece of factory automation is manufactured by another company
for a broad range of applications. However, if the packager can
customize itself for the particular characteristics of the object it
is packaging, it can be much more cost effective. The packager
manufacturer may provide no flexibility or a set of simple controls to
allow a factory worker to adjust for product mean weight, for example.
A packager that can track the characteristics of the process such as
the variance of the weights can make much better decisions as to which
object to pack into which package.

The packaging equipment needs to monitor itself to predict maintenance
needs. For example, the monitoring system of a bearing in the coolant
system could detect a change in the pattern of vibration. This change
might match a change discovered in other bearings manufactured in the
same batch, and indicate an impending failure. Constant monitoring by
dedicated equipment will replace periodic monitoring by human
technicians due to cost and more importantly, reliability. This
provides an opportunity to minimize the costs of actual human
involvement in routine equipment maintenance and replacement, by
scheduling the maintenance when and where it is needed.

Other Applications

We have discussed instrumenting and networking a variety of products
with manufacturers, such as large stationary and mobile diesel
engines, automobiles, and home appliances such as heating and cooling
systems. In each case the products would include an embedded computer
and sensing system. The computer would customize the product for the
particular operator or environment, and predict future maintenance
requirements. Communication with the factory or maintenance
organization would allow sharing of computation between the embedded
computer and central computer resources. More importantly,
communication over the national network would allow sharing of data.
The experiences of similar products could be combined for faster and
more effective learning. The additional resources of the central
facility would allow verification of new control algorithms developed
from the operating data. Manufacturers could essentially redesign
products already in the field, as well as refining the designs of
future products based on data on how those products are actually used.

Research Plan

1) We will develop machine learning, fault detection, and process
optimization algorithms that will implement the behaviors described
above. We will need to develop experience with the algorithms, to the
extent that we trust the algorithms to do something reasonable under a
wide variety of circumstances, with as little human intervention as
possible. We therefore need to work with real data and test our
control algorithms on real equipment.

2) We will develop sources of actual data. We would like to instrument
a set of manufacturing processes and several homes to provide real
data to develop our algorithms on. This data stream would be made
available on the Internet as a general resource.

3) On-line simulation. We need to understand how to couple product
design and manufacturing simulations to real-time information on
product use. We need to understand how to make such simulations deal
with both real-time data and also offer interactive interfaces to
enable human users to play `what if' games, to try different solution
approaches, etc.

4) Large scale simulations. We need to simulate realistically sized
systems. We will develop large scale simulations to provide a general
facility for developers to use to test algorithms. These simulations
would be available on the Internet as a general resource. In addition,
we need to integrate different aspects of manufacturing design, such
as combining fluid flow or combustion models simulating dynamic
processes in combustion engines with structural models simulating
rigid engine components.

5) Intelligent human interfaces. The best way to understand data is to
visualize it, and we will need to develop tools that allow humans to
explore the data stream generated by these embedded systems. Prototype
interfaces will be made available on the Internet as a general
resource.

TECHNOLOGY TRANSFER:

The Principal Investigator is in a proposal to the NIST ATP program
from Siemens and IBM on "A Generic Anomaly Detection Technology".
Funding from the NSF would complement any NIST funding, which is
primarily aimed at the industrial participants. The ATP program also
provides a route for technology transfer from our NSF funded research
to industry. Furthermore, Georgia Tech is formulating its
participation in the "Next Generation Vehicle" program. We feel many
of the techniques described in this proposal will be critical
components in future automobile manufacturing.

Technology transfer is facilitated by the presence of several on
campus centers and programs:

The Manufacturing Research Center (MARC) represents a major commitment
to manufacturing related research at both the Institute and State
levels. At least 4 Industrial sponsors have donated one million
dollars or more to be affiliated with this Center. This unit is housed
in its own building and affords numerous opportunities for
presentation and interaction with potential industrial collaborators.
We have discussed our proposal with the MARC director, and we are
exploring the possibility of instrumenting and experimenting with on
campus manufacturing processes.

The Materials Handling Research Center consists of approximately 30
member companies and 4 universities. Twice annually this group meets
on campus, providing us with the opportunity to disseminate our
research results to this community as well as directly receive
industrial feedback.

Education:

We feel that the proposed effort would have a tremendous educational
impact in addition to affecting the students participating directly in
the research. This effort will heighten interest in embedded
computing and intelligent systems at Georgia Tech. We expect to have a
large number of courses that would explore or use the themes described
as examples.

The computer integrated manufacturing (CIMS) program at Georgia Tech
is a multi-disciplinary endeavor geared to support
manufacturing-related education on campus. Student projects could be
readily integrated into the proposed research and awarded credit for
their participation.

The CIMS/AT&T intelligent mechatronics laboratory provides excellent
resources for hands-on manufacturing education available to the
investigators of this proposal. AT&T has donated $225,000 to support
this laboratory and is keenly interested in projects such as what we
are proposing and how it can impact manufacturing education.

Additionally Georgia Tech has recently won a TRP award in excess of $1
million to further enhance their manufacturing educational laboratory
facilities and manufacturing curriculum.

Finally, an Integrated Process and Product Design (IPPD) Lab, heavily
interdisciplinary in nature, and being funded by the U.S. Army is
being set up to support research in the design of complex intelligent
unmanned systems. These resources would likely also be available in
some capacity for use within this research.